Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: Techniques are provided for centrally analyzing data in a cloud environment. Data can be received from a cloud provider and a machine learning model of a unified smart connector is applied to the data in order to create modeled data. An operation status can be determined based on the modeled data and the unified smart connector can independently determine, for example, if additional data is needed, the type of data needed, and the source of the data.

Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: An account holder's portfolio of transactions may be represented as a network of interconnected transaction nodes where each node represents a credit card transaction. This network may then be analyzed using artificial intelligence and machine learning techniques coupled with visual representations of the interrelated nodes to draw conclusions. An account holder or other system entity may report a fraudulent transaction that employs the holder's account information. A backend system may organize transaction information as a network of data nodes that includes a variety of interrelated information. The backend system may then identify all financial transaction “nodes” within the network that are related or connected by common data. For example, multiple transactions may include a common merchant as the reported fraudulent transaction. The backend may then perform an analysis of the nodes to identify likely fraudulent transactions based on one or more of the data elements for each node.

Abstract: An account holder's portfolio of transactions may be represented as a network of interconnected transaction nodes where each node represents a credit card transaction. This network may then be analyzed using artificial intelligence and machine learning techniques coupled with visual representations of the interrelated nodes to draw conclusions. An account holder or other system entity may report a fraudulent transaction that employs the holder's account information. A backend system may organize transaction information as a network of data nodes that includes a variety of interrelated information. The backend system may then identify all financial transaction “nodes” within the network that are related or connected by common data. For example, multiple transactions may include a common merchant as the reported fraudulent transaction. The backend may then perform an analysis of the nodes to identify likely fraudulent transactions based on one or more of the data elements for each node.

Abstract: A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.

Abstract: Techniques are provided for centrally analyzing data in a cloud environment. Data can be received from a cloud provider and a machine learning model of a unified smart connector is applied to the data in order to create modeled data. An operation status can be determined based on the modeled data and the unified smart connector can independently determine, for example, if additional data is needed, the type of data needed, and the source of the data.

Abstract: An account holder's portfolio of transactions may be represented as a network of interconnected transaction nodes where each node represents a credit card transaction. This network may then be analyzed using artificial intelligence and machine learning techniques coupled with visual representations of the interrelated nodes to draw conclusions. An account holder or other system entity may report a fraudulent transaction that employs the holder's account information. A backend system may organize transaction information as a network of data nodes that includes a variety of interrelated information. The backend system may then identify all financial transaction “nodes” within the network that are related or connected by common data. For example, multiple transactions may include a common merchant as the reported fraudulent transaction. The backend may then perform an analysis of the nodes to identify likely fraudulent transactions based on one or more of the data elements for each node.

Abstract: A method for fulfillment of a reward amount earned by a user includes: receiving reward data associated with a rewards program of an offeror; receiving a transaction request associated with a transaction between a user and a merchant; determining whether the user qualifies for the rewards program; upon determining that the user qualifies for the rewards program determining the reward amount for the user corresponding to the rewards program; and crediting the reward amount to an account of the user. Systems for fulfillment of a reward amount earned by a user are also disclosed.

Abstract: There is provided a method for culturing a stem cell in vitro. The method comprises providing a substrate surface coated with a coating comprising a molecule having a catechol moiety or a polymer thereof; and growing a stem cell on said coated substrate surface in a growth medium.

Abstract: There is provided a method for culturing a stem cell in vitro. The method comprises providing a substrate surface coated with a coating comprising a molecule having a catechol moiety or a polymer thereof; and growing a stem cell on said coated substrate surface in a growth medium.